Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory72.3 B

Variable types

Numeric7
Categorical2

Alerts

CGPA is highly overall correlated with Chance of Admit and 4 other fieldsHigh correlation
Chance of Admit is highly overall correlated with CGPA and 5 other fieldsHigh correlation
GRE Score is highly overall correlated with CGPA and 5 other fieldsHigh correlation
LOR is highly overall correlated with CGPA and 4 other fieldsHigh correlation
Research is highly overall correlated with Chance of Admit and 1 other fieldsHigh correlation
SOP is highly overall correlated with CGPA and 4 other fieldsHigh correlation
TOEFL Score is highly overall correlated with CGPA and 4 other fieldsHigh correlation
Serial No. is uniformly distributedUniform
Serial No. has unique valuesUnique

Reproduction

Analysis started2024-08-11 17:20:00.547722
Analysis finished2024-08-11 17:20:06.456827
Duration5.91 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Serial No.
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:06.567571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2024-08-11T22:50:06.716720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
330 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

GRE Score
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.472
Minimum290
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:06.857690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile298
Q1308
median317
Q3325
95-th percentile335
Maximum340
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.295148
Coefficient of variation (CV)0.03569083
Kurtosis-0.71106446
Mean316.472
Median Absolute Deviation (MAD)8
Skewness-0.039841858
Sum158236
Variance127.58038
MonotonicityNot monotonic
2024-08-11T22:50:07.014013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
312 24
 
4.8%
324 23
 
4.6%
316 18
 
3.6%
321 17
 
3.4%
322 17
 
3.4%
327 17
 
3.4%
311 16
 
3.2%
320 16
 
3.2%
314 16
 
3.2%
317 15
 
3.0%
Other values (39) 321
64.2%
ValueCountFrequency (%)
290 2
 
0.4%
293 1
 
0.2%
294 2
 
0.4%
295 5
1.0%
296 5
1.0%
297 6
1.2%
298 10
2.0%
299 10
2.0%
300 12
2.4%
301 11
2.2%
ValueCountFrequency (%)
340 9
1.8%
339 3
 
0.6%
338 4
0.8%
337 2
 
0.4%
336 5
1.0%
335 4
0.8%
334 8
1.6%
333 4
0.8%
332 8
1.6%
331 9
1.8%

TOEFL Score
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.192
Minimum92
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:07.424180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98
Q1103
median107
Q3112
95-th percentile118
Maximum120
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.0818677
Coefficient of variation (CV)0.056738074
Kurtosis-0.6532454
Mean107.192
Median Absolute Deviation (MAD)5
Skewness0.095600972
Sum53596
Variance36.989114
MonotonicityNot monotonic
2024-08-11T22:50:07.533554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
110 44
 
8.8%
105 37
 
7.4%
104 29
 
5.8%
107 28
 
5.6%
106 28
 
5.6%
112 28
 
5.6%
103 25
 
5.0%
100 24
 
4.8%
102 24
 
4.8%
99 23
 
4.6%
Other values (19) 210
42.0%
ValueCountFrequency (%)
92 1
 
0.2%
93 2
 
0.4%
94 2
 
0.4%
95 3
 
0.6%
96 6
 
1.2%
97 7
 
1.4%
98 10
2.0%
99 23
4.6%
100 24
4.8%
101 20
4.0%
ValueCountFrequency (%)
120 9
 
1.8%
119 10
 
2.0%
118 10
 
2.0%
117 8
 
1.6%
116 16
3.2%
115 11
 
2.2%
114 18
3.6%
113 19
3.8%
112 28
5.6%
111 20
4.0%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
162 
2
126 
4
105 
5
73 
1
34 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

Length

2024-08-11T22:50:07.667692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-11T22:50:07.777067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

Most occurring characters

ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 162
32.4%
2 126
25.2%
4 105
21.0%
5 73
14.6%
1 34
 
6.8%

SOP
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.374
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:07.886708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.99100362
Coefficient of variation (CV)0.29371773
Kurtosis-0.70571695
Mean3.374
Median Absolute Deviation (MAD)0.5
Skewness-0.2289724
Sum1687
Variance0.98208818
MonotonicityNot monotonic
2024-08-11T22:50:07.995816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 89
17.8%
3.5 88
17.6%
3 80
16.0%
2.5 64
12.8%
4.5 63
12.6%
2 43
8.6%
5 42
8.4%
1.5 25
 
5.0%
1 6
 
1.2%
ValueCountFrequency (%)
1 6
 
1.2%
1.5 25
 
5.0%
2 43
8.6%
2.5 64
12.8%
3 80
16.0%
3.5 88
17.6%
4 89
17.8%
4.5 63
12.6%
5 42
8.4%
ValueCountFrequency (%)
5 42
8.4%
4.5 63
12.6%
4 89
17.8%
3.5 88
17.6%
3 80
16.0%
2.5 64
12.8%
2 43
8.6%
1.5 25
 
5.0%
1 6
 
1.2%

LOR
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.484
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:08.105191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92544957
Coefficient of variation (CV)0.26562847
Kurtosis-0.74574851
Mean3.484
Median Absolute Deviation (MAD)0.5
Skewness-0.14529031
Sum1742
Variance0.85645691
MonotonicityNot monotonic
2024-08-11T22:50:08.214575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 99
19.8%
4 94
18.8%
3.5 86
17.2%
4.5 63
12.6%
2.5 50
10.0%
5 50
10.0%
2 46
9.2%
1.5 11
 
2.2%
1 1
 
0.2%
ValueCountFrequency (%)
1 1
 
0.2%
1.5 11
 
2.2%
2 46
9.2%
2.5 50
10.0%
3 99
19.8%
3.5 86
17.2%
4 94
18.8%
4.5 63
12.6%
5 50
10.0%
ValueCountFrequency (%)
5 50
10.0%
4.5 63
12.6%
4 94
18.8%
3.5 86
17.2%
3 99
19.8%
2.5 50
10.0%
2 46
9.2%
1.5 11
 
2.2%
1 1
 
0.2%

CGPA
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.57644
Minimum6.8
Maximum9.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:08.370815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile7.638
Q18.1275
median8.56
Q39.04
95-th percentile9.6
Maximum9.92
Range3.12
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.6048128
Coefficient of variation (CV)0.070520263
Kurtosis-0.5612784
Mean8.57644
Median Absolute Deviation (MAD)0.46
Skewness-0.026612517
Sum4288.22
Variance0.36579852
MonotonicityNot monotonic
2024-08-11T22:50:08.511441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.76 9
 
1.8%
8 9
 
1.8%
8.12 7
 
1.4%
8.45 7
 
1.4%
8.54 7
 
1.4%
8.56 7
 
1.4%
8.65 6
 
1.2%
7.88 6
 
1.2%
9.11 6
 
1.2%
9.04 6
 
1.2%
Other values (174) 430
86.0%
ValueCountFrequency (%)
6.8 1
0.2%
7.2 1
0.2%
7.21 1
0.2%
7.23 1
0.2%
7.25 1
0.2%
7.28 1
0.2%
7.3 1
0.2%
7.34 2
0.4%
7.36 1
0.2%
7.4 1
0.2%
ValueCountFrequency (%)
9.92 1
 
0.2%
9.91 1
 
0.2%
9.87 2
0.4%
9.86 1
 
0.2%
9.82 1
 
0.2%
9.8 3
0.6%
9.78 1
 
0.2%
9.76 2
0.4%
9.74 1
 
0.2%
9.7 2
0.4%

Research
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
280 
0
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Length

2024-08-11T22:50:08.637981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-11T22:50:08.716313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring characters

ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Chance of Admit
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72174
Minimum0.34
Maximum0.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-08-11T22:50:08.825473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.47
Q10.63
median0.72
Q30.82
95-th percentile0.94
Maximum0.97
Range0.63
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1411404
Coefficient of variation (CV)0.19555575
Kurtosis-0.4546818
Mean0.72174
Median Absolute Deviation (MAD)0.1
Skewness-0.28996621
Sum360.87
Variance0.019920614
MonotonicityNot monotonic
2024-08-11T22:50:08.966098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71 23
 
4.6%
0.64 19
 
3.8%
0.73 18
 
3.6%
0.72 16
 
3.2%
0.79 16
 
3.2%
0.78 15
 
3.0%
0.76 14
 
2.8%
0.62 13
 
2.6%
0.94 13
 
2.6%
0.7 13
 
2.6%
Other values (51) 340
68.0%
ValueCountFrequency (%)
0.34 2
 
0.4%
0.36 2
 
0.4%
0.37 1
 
0.2%
0.38 2
 
0.4%
0.39 1
 
0.2%
0.42 4
0.8%
0.43 1
 
0.2%
0.44 3
0.6%
0.45 3
0.6%
0.46 5
1.0%
ValueCountFrequency (%)
0.97 4
 
0.8%
0.96 8
1.6%
0.95 5
 
1.0%
0.94 13
2.6%
0.93 12
2.4%
0.92 9
1.8%
0.91 10
2.0%
0.9 9
1.8%
0.89 11
2.2%
0.88 4
 
0.8%

Interactions

2024-08-11T22:50:05.506410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:00.742937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.440952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:02.168845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.434230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.124598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.820947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.610948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:00.845941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.534703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:02.262596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.527973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.218351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.914698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.709787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:00.955318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.629066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:02.356342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.624599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.312108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.009099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.785940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.034703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.731338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:02.450088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.718363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.405853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.103926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.895206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.144078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.871963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.148374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.812102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.515229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.197677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.988955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.253453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.981342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.259611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.921479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.617826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.318910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:06.098339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:01.347206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:02.075094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:03.340652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.015229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:04.727201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-11T22:50:05.412661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-08-11T22:50:09.068953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
CGPAChance of AdmitGRE ScoreLORResearchSOPSerial No.TOEFL ScoreUniversity Rating
CGPA1.0000.8890.8290.6400.4960.717-0.0750.8090.426
Chance of Admit0.8891.0000.8220.6440.5550.703-0.0020.7940.430
GRE Score0.8290.8221.0000.5140.5870.621-0.1000.8240.388
LOR0.6400.6440.5141.0000.3570.6630.0040.5230.348
Research0.4960.5550.5870.3571.0000.409-0.0050.4750.431
SOP0.7170.7030.6210.6630.4091.000-0.1440.6450.477
Serial No.-0.075-0.002-0.1000.004-0.005-0.1441.000-0.1430.072
TOEFL Score0.8090.7940.8240.5230.4750.645-0.1431.0000.393
University Rating0.4260.4300.3880.3480.4310.4770.0720.3931.000

Missing values

2024-08-11T22:50:06.229058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-11T22:50:06.394328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1232410744.04.58.8710.76
2331610433.03.58.0010.72
3432211033.52.58.6710.80
4531410322.03.08.2100.65
5633011554.53.09.3410.90
6732110933.04.08.2010.75
7830810123.04.07.9000.68
8930210212.01.58.0000.50
91032310833.53.08.6000.45
Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
49049130710522.54.58.1210.67
4914922979943.03.57.8100.54
49249329810142.54.57.6910.53
4934943009523.01.58.2210.62
4944953019932.52.08.4510.68
49549633210854.54.09.0210.87
49649733711755.05.09.8710.96
49749833012054.55.09.5610.93
49849931210344.05.08.4300.73
49950032711344.54.59.0400.84